Deep semi-supervised learning is a fast-growing field with a range of practical applications.
This paper provides a comprehensive survey on both fundamentals and recent advances in …

Generative adversarial networks (GANs) have recently become a hot research topic;
however, they have been studied since 2014, and a large number of algorithms have been …

Deep learning has produced state-of-the-art results for a variety of tasks. While such
approaches for supervised learning have performed well, they assume that training and …

Unsupervised image-to-image translation is an important and challenging problem in
computer vision. Given an image in the source domain, the goal is to learn the conditional …

Learning inter-domain mappings from unpaired data can improve performance in structured
prediction tasks, such as image segmentation, by reducing the need for paired data …

Responses generated by neural conversational models tend to lack informativeness and
diversity. We present Adversarial Information Maximization (AIM), an adversarial learning …

Generative adversarial networks (GANs) have been effective for learning generative models
for real-world data. However, accompanied with the generative tasks becoming more and …

Throughout the course of delivering a radiation therapy treatment, which may take several
weeks, a patient's anatomy may change drastically, and adaptive radiation therapy (ART) …

We investigate the non-identifiability issues associated with bidirectional adversarial training
for joint distribution matching. Within a framework of conditional entropy, we propose both …

Abstract Knowledge distillation (KD) aims to train a lightweight classifier suitable to provide
accurate inference with constrained resources in multi-label learning. Instead of directly …